ArticleBriefings in bioinformatics2024
Accurately deciphering spatial domains for spatially resolved transcriptomics with stCluster.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- MicNet: integrating spatially resolved transcriptomes and pathology images by contrastive deep neural network.Genome biology · 2026Article
- DisConST: Distribution-aware Contrastive Learning for Spatial Domain Identification.Genomics, proteomics & bioinformatics · 2026Article
- stSCI: A multi-task learning framework for integrative analysis of single-cell and spatial transcriptomics data.Innovation (Cambridge (Mass.)) · 2026Article
- STGNET: extending panel coverage in imaging-based spatial transcriptomics using deep generative adversarial networks.Briefings in bioinformatics · 2026Article
- Using multimodal cortical parcellations to identify novel regions of the human cerebral cortex associated with cognitive performance.Translational psychiatry · 2026Article
- Enhancing cell subpopulation discovery in cancer by integrating single-cell transcriptome and expressed variants.Fundamental research · 2026Article
- MUSE: A Multi-slice Joint Analysis Method for Spatial Transcriptomics Experiments.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2025Article
- Spatial domain identification method based on multi-view graph convolutional network and contrastive learning.PLoS computational biology · 2025Article
- Reg2ST: recognizing potential patterns from gene expression for spatial transcriptomics prediction.Briefings in bioinformatics · 2025Article
- spaLLM: enhancing spatial domain analysis in multi-omics data through large language model integration.Briefings in bioinformatics · 2025Article
- HABiC: an algorithm based on the exact computation of the Kantorovich-Rubinstein optimizer for binary classification in transcriptomics.Bioinformatics (Oxford, England) · 2025Article
- Benchmarking computational methods for detecting spatial domains and domain-specific spatially variable genes from spatial transcriptomics data.Nucleic acids research · 2025Article
- MAEST: accurately spatial domain detection in spatial transcriptomics with graph masked autoencoder.Briefings in bioinformatics · 2025Article
- A cross dataset meta-model for hepatitis C detection using multi-dimensional pre-clustering.Scientific reports · 2025Article
- The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning.Scientific reports · 2025Article
- CDFA: Calibrated deep feature aggregation for screening synergistic drug combinations.Frontiers in pharmacology · 2025Article
- SpaLLM: a general framework for spatial domain identification with large language models.Frontiers in bioinformatics · 2025Article
- DGAT: a dynamic graph attention neural network framework for EEG emotion recognition.Frontiers in psychiatry · 2025Article
- Prediction of viral oncoproteins through the combination of generative adversarial networks and machine learning techniques.Scientific reports · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
Spatial transcriptomics provides valuable insights into gene expression within the native tissue context, effectively merging molecular data with spatial information to uncover intricate cellular relationships and tissue organizations. In this context, deciphering cellular spatial domains becomes essential for revealing complex cellular dynamics and tissue structures. However, current methods encounter challenges in seamlessly integrating gene expression data with spatial information, resulting in less informative representations of spots and suboptimal accuracy in spatial domain identification. We introduce stCluster, a novel method that integrates graph contrastive learning with multi-task learning to refine informative representations for spatial transcriptomic data, consequently improving spatial domain identification. stCluster first leverages graph contrastive learning technology to obtain discriminative representations capable of recognizing spatially coherent patterns. Through jointly optimizing multiple tasks, stCluster further fine-tunes the representations to be able to capture complex relationships between gene expression and spatial organization. Benchmarked against six state-of-the-art methods, the experimental results reveal its proficiency in accurately identifying complex spatial domains across various datasets and platforms, spanning tissue, organ, and embryo levels. Moreover, stCluster can effectively denoise the spatial gene expression patterns and enhance the spatial trajectory inference. The source code of stCluster is freely available at https://github.com/hannshu/stCluster.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.